Rethinking Human Oversight for High-Risk Autonomous AI
A look at high-risk AI oversight through the humans-as-handlers approach, focusing on intervention, accountability, and trust.
A look at high-risk AI oversight through the humans-as-handlers approach, focusing on intervention, accountability, and trust.
Examines whether AI safety remains consistent in long conversations and highlights gaps in session-level evaluation.
Examines whether AI eliminates jobs or redesigns tasks, and why this shift matters for hiring, reskilling, and productivity.
A practical guide to balancing agent autonomy, traceability, and control in enterprise orchestration design.
EM may depend on optimizers and batch settings, making finetuning recipes part of safety evaluation, not just data.
Why AI agents must move beyond preference elicitation to support preference formation, with evaluation and safety in view.
How a dual-agent LLM pipeline separates proposing tighter relaxations from verification in automated research.
In education, AI design matters more than raw performance, with student privacy, data minimization, and teacher control at stake.
Commercial APIs and open-weight models differ not just in performance, but in who runs blocking, logging, and policy enforcement.
Examines Google PAT's paper-checking results and limits, and where AI should fit in academic review workflows.
Why equilibrium selection, conservatism, and data coverage matter when solving offline multi-agent games from fixed logs.
Rhythm game AI works best when API and local inference are split by function, balancing latency, limits, cost, and memory.
How to assess whether AI firms' calls for regulation signal safety commitments, competitive strategy, or both.
A compact fast-weight recurrent model reported lower pooled RMSE than a larger LSTM using only 22.4% of the parameters.
Office humanoid robots should be judged by learning pipelines, generalization, and public validation, not demos alone.
Autonomous coding agents should be evaluated beyond PR pass rates, with repository-level risk and structural health in view.
Examines how class imbalance affects score learning in diffusion models and why frequency-guided noise schedules matter.
Compare cloud token-based LLM pricing with local deployment to assess cost, control, latency, and break-even conditions.
CoIn links 2D inpainting and 3DGS to reduce reliance on precise multiview masks in 3D scene editing workflows.
Strong language performance may not imply a stable world model. Reassessing LLMs through failures in time, space, and physics.
How GRACE combines QAT and distillation to balance accuracy and deployment cost in vision-language models.
Why top satellite SR models on synthetic data may not lead on real cross-sensor imagery, and how to evaluate the gap.
MMG-Pop uses multimodal and temporal graph signals from Bluesky and Reddit to reassess social popularity prediction.
How ontology constraints reduce noisy paths in multi-hop KGQA and improve reasoning for complex queries.